A Narrower Scope or a Clearer Lens for Personality? Examining Sources of Observers’ Advantages Over Self‐Reports for Predicting Performance
Bibliographic record
Abstract
Emerging studies have shown that observers' ratings of personality predict performance behaviors better than do self-ratings. However, it is unclear whether these predictive advantages stem from (a) use of observers who have a frame of reference more closely aligned with the criterion ("narrower scope") or (b) observers having greater accuracy than targets themselves ("clearer lens"). In a primary study of 291 raters of 97 targets, we found predictive advantages even when observers were personal acquaintances who knew targets only outside of the work context. Integrating these findings with previous meta-analyses showed that colleagues' unique perspectives did not predict incrementally beyond commonly held trait perceptions across all raters (except for openness) and that self-raters who overestimate their agreeableness and conscientiousness perform worse on the job. Broadly, our results suggest that observers have clearer lenses for viewing targets' personality traits, and we discuss the theoretical implications of these findings for studying and measuring personality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".